Scientific Computing with Python: High-performance scientific computing with NumPy, SciPy, and pandas , Second Edition

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In this chapter, we will present the most important and basic types in Python. What is a type? It is a set consisting of data content, its representation, and all possible operations. Later in this book, we will make this definition much more precise when we introduce the concepts of a class in Chapter 8 : Classes.

In this chapter, we'll cover the following topics:

Variables
Numeric types
Booleans
Strings

Key benefits

Learn the first steps within Python to highly specialized concepts

Explore examples and code snippets taken from typical programming situations within scientific computing.

Delve into essential computer science concepts like iterating, object-oriented programming, testing, and MPI presented in strong connection to applications within scientific computing.

Description

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.

What you will learn

Understand the building blocks of computational mathematics, linear algebra, and related Python objects

Use Matplotlib to create high-quality figures and graphics to draw and visualize results

Apply object-oriented programming (OOP) to scientific computing in Python

Discover how to use pandas to enter the world of data processing

Handle exceptions for writing reliable and usable code

Cover manual and automatic aspects of testing for scientific programming

Get to grips with parallel computing to increase computation speed

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